DERIVATION OF A SCORE TO PREDICT ADMISSION TO … · 5/12/2020 · 1 DERIVATION OF A SCORE TO PREDICT ADMISSION TO INTENSIVE CARE UNIT IN PATIENTS WITH COVID-19: THE ABC-GOALS SCORE

1

DERIVATION OF A SCORE TO PREDICT ADMISSION TO INTENSIVE CARE

UNIT IN PATIENTS WITH COVID-19: THE ABC-GOALS SCORE

Juan M. Mejía-Vilet, MD, MSc 1

Bertha M. Córdova-Sánchez, MD, MSc 2

Dheni A. Fernández-Camargo, MD 1

R. Angélica Méndez-Pérez, MD 1

Luis E. Morales Buenrostro, MD, PhD 1

Thierry Hernández-Gilsoul, MD, MSc 3

1 Department of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias

Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

2 Department of Critical Care Medicine, Instituto Nacional de Cancerología, Mexico

City, Mexico

3 Emergency Department, Instituto Nacional de Ciencias Médicas y Nutrición

Salvador Zubirán, Mexico City, Mexico

Correspondence:

Thierry Hernández-Gilsoul

Head of the Emergency Department, Instituto Nacional de Ciencias Médicas y

Nutrición Salvador Zubirán, Mexico City, Mexico

Vasco de Quiroga 15, Belisario Domínguez Sección XVI, Tlalpan, Mexico City,

Mexico, 14080

Email: [email protected], Phone: +52 55 54870900 Extension 5067

Running Title: ABC-GOALS score to predict ICU admission

Keywords: COVID-19, coronavirus, prediction, intensive care unit.

. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)

The copyright holder for this preprint this version posted May 16, 2020. ; https://doi.org/10.1101/2020.05.12.20099416doi: medRxiv preprint

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

https://doi.org/10.1101/2020.05.12.20099416

http://creativecommons.org/licenses/by-nc-nd/4.0/

2

SUMMARY

This prospective study derived a prognostic score for admission to ICU in COVID-

19 patients. The predictive models include a clinical, clinical plus laboratory, and

clinical plus laboratory plus imaging score that may apply to different clinical

situations aiding clinical decision making and patient referral.



https://doi.org/10.1101/2020.05.12.20099416


3

ABSTRACT

Background. COVID-19 pandemic poses a burden on hospital resources and

intensive care unit (ICU) occupation. The study aimed to derive a scoring system

that, assessed upon patient first-contact evaluation, predicts the need for

admission to ICU.

Methods. All consecutive patients admitted to a COVID-19 reference center were

prospectively assessed. Patients were segregated into a group that required

admission to ICU during their hospitalization and a group that never required ICU

admission and was already discharged from hospitalization. Three models

including clinical, laboratory and imaging findings were derived by logistic

regression analysis and internally validated. A score, defined as the ABC-GOALS

score was created by assigning values based on the variables weighted odd ratios.

Results. The study comprised 329 patients, 115 (35%) required ICU admission

and 214 (65%) were hospitalized and discharged from general wards. The clinical

prediction model (ABC-GOALSc) included sex, obesity, the Charlson comorbidity

index, dyspnea, arterial pressure and respiratory rate at triage evaluation. The

clinical plus laboratory model (ABC-GOALScl) added serum albumin, glucose,

lactate dehydrogenase and S/F ratio to the clinical model. The model that included

imaging (ABC-GOALSclx) added the CT scan finding of >50% lung involvement. All

three models outperformed other pneumonia-specific scores with area under the

curve of 0.79 (0.74-0.83), 0.86 (0.82-0.90) and 0.88 (0.84-0.92) for the clinical,

laboratory and imaging model, respectively.

Conclusion. The ABC-GOALS score is a tool to evaluate patients with COVID-19

at admission to the emergency department that allows to timely predict their risk of

admission to an ICU and may help optimize healthcare capacities.



https://doi.org/10.1101/2020.05.12.20099416


4

INTRODUCTION

The presence of a new severe acute respiratory syndrome coronavirus 2 (SARS-

CoV-2) was first reported in China and has subsequently spread to all regions of

the world, straining the health systems of many countries1. Viral pneumonia

associated with SARS-CoV-2 has been officially denominated as coronavirus

disease 2019 (COVID-19)2.

Approximately 5 to 33% of patients with COVID-19 pneumonia will be admitted to

an intensive care unit (ICU)3–5. Previous studies have identified several risk factors

associated with a severe course of COVID-19 pneumonia and its progression to

acute respiratory distress syndrome 6,7. These risk factors may be categorized into

patient characteristics obtained through the medical interview (e.g. age,

comorbidities, symptoms)6–11; vital signs obtained from triage evaluation (e.g.

respiratory rate, arterial pressure)12,13; laboratory abnormalities including

inflammatory, coagulation and organ-specific studies (e.g. lactate dehydrogenase,

D-dimer, fibrinogen, cardiac troponins, liver function tests, among others) 6,7,9–11,14;

and lung imaging findings (e.g. number of affected lobes, estimated pneumonia

extension) 15,16.

In several countries, including Mexico, hospital reconversion and temporary care

centers have been implemented to cope with the large number of COVID-19

patients 17. However, many of these centers are prepared to care for patients with

supplemental oxygen requirements but just a few patients who require admission

to an intensive care unit (ICU) and mechanical ventilation.



https://doi.org/10.1101/2020.05.12.20099416


5

The aim of the study was to derive risk prediction models to anticipate the need for

admission to an ICU. These models include clinical, laboratory and image findings

obtained at the first-contact evaluation to aid triage, decision making and timely

referral to maintain healthcare system capacity.

METHODS

This is a prospective observational cohort study. All consecutive adult (>18 years)

patients hospitalized at Instituto Nacional de Ciencias Médicas y Nutrición

Salvador Zubirán, a referral center for COVID-19 patients at Mexico City, were

evaluated for this analysis. These patients were hospitalized between March 16th

and April 30th and followed until May 8th. The study was approved by the local

Research and Ethics Board (CAI-3368-20-20-1) that waived the use of an informed

consent form due to the study’s nature.

The study population was segregated into two groups: 1) patients who required

admission to an ICU at any time during their hospitalization, and 2) patients

admitted to general wards who were discharged from hospitalization without ever

been considered for admission to an ICU. Patients were censored for each group

once they were admitted to the ICU or by the date of discharge from

hospitalization, respectively. All patients who remained hospitalized in general

wards by the end of the study were discarded for this analysis as it was considered

that their risk for ICU admission is still active.



https://doi.org/10.1101/2020.05.12.20099416


6

All patients had COVID-19 pneumonia diagnosed by chest computed tomography

(CT) and SARS-CoV-2 infection confirmed in respiratory specimens using real-time

reverse-transcriptase polymerase chain reaction (RT-PCR) assay by local testing

and confirmed at a central laboratory. Patients were considered positive if the initial

test results were positive, or if it was negative but repeat testing was positive.

The following variables were obtained by the time of the triage and emergency

department (ED) evaluation: demographic variables, previous medical and

medication history including smoking; symptoms, physical examination including

weight, height and vital signs; laboratory evaluation including arterial blood gas

analysis, inflammatory biomarkers, troponin-I levels, complete blood count and

blood chemistries; and chest CT-scan findings. All patients underwent chest CT-

scans that were evaluated by experienced specialists; lung involvement was semi

quantitatively classified as mild (<20%), moderate (20-50%) or severe (>50%).

For each patient, we calculated the Charlson comorbidity index (an index that

predicts 10-year survival in patients with multiple comorbidities) 18, National Early

Warning Score (NEWS) 2 19, Sequential Organ Failure Assessment (SOFA) score

20, CURB-65 score for pneumonia severity 21, the MuLBSTA score for viral

pneumonia mortality 22, the ROX index to predict the risk of intubation 23 and the

CALL score model for prediction of progression risk in COVID-19 24. All scores

were calculated at admission to the ED. Although most of these scores were not

derived to evaluate the risk of admission to an ICU, we evaluated their value if

repurposed for this end.



https://doi.org/10.1101/2020.05.12.20099416


7

The need for admission to critical care was determined by the medical team in

charge of the patient and included the need for mechanical ventilation or high-dose

vasopressors. Patients hospitalized in medical wards were treated with

supplementary oxygen (nasal cannula or non-rebreathing oxygen mask) but were

never considered for intubation and ICU admission. All patients in this group were

censored once discharged from hospitalization after improvement. Clinical

outcomes for patients admitted to ICU were monitored up to May 8th, 2020.

Statistical analysis

The distribution of continuous variables was evaluated by the Kolmogorov-Smirnov

test. Variables are described as number (relative frequency) or median

(interquartile range [IQR]) as appropriate. Characteristics at admission between

study groups were compared by the Mann-Whitney U test. There were less than

2% missing values for all variables. In the case of missing data, variables were

imputed by using multiple imputations 25.

For score derivation, all variables were evaluated by bivariate logistic regression

analysis. All variables with p-values <0.05 were considered for the multivariate

logistic regression analysis. The best logistic regression analysis model was

constructed by the forward stepwise selection method using maximum likelihood

estimation and r-square values. Three models were constructed: a first model

including only clinical variables (ABC-GOALSc), a second model that included

clinical and laboratory variables (ABC-GOALScl) and a third model that included



https://doi.org/10.1101/2020.05.12.20099416


8

clinical, laboratory and x-ray findings (ABC-GOALSclx). The predictive performance

of each model was measured by the concordance index (C-index) and internal

calibration was evaluated by 1000 bootstrap samples. The goodness of fit was

evaluated by the Hosmer-Lemeshow test. To create the final scores, points were

assigned by the weighted odd-ratios and approached to the closest integer for

each model.

The performance of the derived scores as well as for all other scores determined at

admission to predict the risk of hospitalization into an ICU were assessed by

receiver operating characteristic (ROC) curves and their 95% confidence intervals.

All analyses were performed with SPSS version 24.0 (IBM, Armonk, NY, USA) and

GraphPad Prism 6.0 (GraphPad Software, San Diego, CA, USA).

RESULTS

Patient characteristics at admission

A total of 410 patients with COVID-19 pneumonia and SARS-CoV-2 positive test

were hospitalized during the study period. Seventy-nine patients who remained

hospitalized in general wards and two patients who were discharged by discharge

against medical advice were excluded from the analysis. A total of 115 patients

that required ICU admission and 214 patients that had been hospitalized and

discharged from general wards were finally included for analysis. The median age

of the study population was 49 years (IQR 41-60), 211 (64%) subjects were male

with a median Charlson comorbidity index of 1 point (IQR 0-2). There were 100



https://doi.org/10.1101/2020.05.12.20099416


9

(30%) subjects with no previous comorbidity. The median days from the start of

symptoms to the evaluation at the ER were 7 days (IQR 5-10). All patients received

supplementary oxygen. For the group admitted to ICU, the median time from ER

admission to ICU admission was 2 days (IQR 0-3 days, range 0 to 12). The median

length of hospitalization for the group hospitalized in general wards was 6 days

(IQR 4-9 days, range 1 to 20).

Several differences in the admission variables were observed between patients

who required critical care and those hospitalized in general wards and are

described in Table 1 and Supplementary Table 1.

Factors associated with the need of admission to a critical care unit

An extended table with all the results obtained from the bivariate logistic regression

analysis is shown in Supplementary Table 2. The best predictive models derived

from multivariable logistic regression analysis are shown in Table 2. The clinical

model included: male sex, the Charlson comorbidity index, obesity (BMI>30kg/m2,

not included in the Charlson index), referred dyspnea, respiratory rate and systolic

arterial pressure at the triage or ER evaluation. The clinical plus laboratory model

included the same clinical variables plus serum albumin <3.5g/dL, lactate

dehydrogenase above the upper limit of normal and the hemoglobin oxygen

saturation to fraction of inspired oxygen ratio <300 (S/F ratio). The clinical plus

laboratory model included all previous variables except for referred dyspnea and

serum albumin, and added reported lung involvement >50% in the lung CT scan.



https://doi.org/10.1101/2020.05.12.20099416


10

The c-statistics for clinical, clinical plus laboratory, and clinical plus laboratory plus

x-ray models were 0.79, 0.87 and 0.88, respectively.

Derivation of the ABC-GOALS scores

A predictive point score was constructed based on the weighted OR’s from each

logistic regression model. The derived scores were defined as the ABC-GOALS

(Arterial pressure, Breathlessness, Charlson, Glucose, Obesity, Albumin, LDH and

S/F ratio) and labeled as clinical (ABC-GOALSc), clinical plus laboratory (ABC-

GOALScl) and clinical plus laboratory plus x-ray (ABC-GOALSclx) scores (Table 3).

All three ABC-GOALS score variations demonstrated good accuracy in estimating

the risk of admission to an ICU, with area under the curve of 0.79 (95%CI 0.74-

0.83), 0.86 (95%CI 0.82-0.90) and 0.88 (95%CI 0.84-0.92) for the ABC-GOALSc,

ABC-GOALScl and ABC-GOALSclx scores, respectively (Figure 1). Calibration plots

showed good agreement between the estimated and observed scores (Figure 2).

By segregating the scores into three levels of risk of admission to ICU, the ABC-

GOALSc classified patients into low- (0-2 points, mean risk 8%, 95%CI 7-9%),

moderate- (3-7 points, mean risk 32%, 95%CI 30-34%) and high-risk (≥8 points,

mean risk 73%, 95%CI 70-75%) of admission ICU. The respective groups for the

ABC-GOALScl were low- (0-3 points, mean risk 4%, 95%CI 3-4%), moderate- (4-9

points, mean risk 30%, 95%CI 27-32%) and high-risk (≥10 points, mean risk 80%,

95%CI 78-82%) for ICU admission. Finally, the groups for the ABC-GOALSclx were

low- (0-4 points, mean risk 6%, 95%CI 5-6%), moderate- (5-9 points, mean 32%,



https://doi.org/10.1101/2020.05.12.20099416


11

95%CI 29-34%) and high-risk (≥10 points, mean 80%, 95%CI 78-82%) for ICU

admission (Table 3). The sensitivity and specificity for selected cutoffs of these

scores are shown in Table 4.

The ABC-GOALS variants outperformed other scores that were repurposed for the

prediction of admission to ICU, based on the area under the curve values (Table

4). As previously stated, most of these scores were not created to predict this

outcome. For practicality, the three ABC-GOALS scores are planned to be

implemented into an application for mobile devices.

Outcomes

By the end of this study (May 8th), all patients from the group that was hospitalized

in general wards were discharged to home after improvement. Only two patients

(0.9%) were readmitted and discharged again without admission to ICU during the

study period. Sixty-one (53%) patients from the group admitted to ICU have died,

25 (22%) have been discharged to their homes after improvement, and 29 (25%)

remain hospitalized. The ABC-GOALSc, ABC-GOALScl and ABC-GOALSclx scores

predicted mortality with a sensitivity of 31%, 58%, and 64%, and specificity of 97%,

92% and 94%, respectively, (AUC of 0.79, 0.86 and 0.88 for mortality,

respectively).



https://doi.org/10.1101/2020.05.12.20099416


12

DISCUSSION

We derived a prognostic score that, evaluated upon patient admission to the ED,

helps predict the probability of admission to ICU during patient hospitalization. This

tool may prove useful for patient assessment at first-contact evaluation and to

timely refer patients to other units in case of a lack of or overcrowding of local

ICUs. The ABC-GOALS score predicts the need for admission to an ICU with

greater accuracy than other scores that were created for the prediction of other

outcomes in sepsis or pneumonia, such as mortality.

To date, more than 4 million SARS-CoV-2 infections have been reported worldwide

with almost 300,000 total deaths26. The fast spread of the disease 27 and the large

number of patients admitted to hospitals has strained and overwhelmed local

health systems. Among hospitalized patients, 5 to 33% will require admission to an

ICU and 75% to 100% of them will require support with mechanical ventilation 3–5,8.

An effective evaluation of patients with severe disease may be critical to maintain

healthcare system functional for as long as possible.

The ABC-GOALS score summarizes many of the previously reported factors that

have been shown to independently associate with severe COVID-19 disease. We

found that age and comorbidities predictive performance is enhanced when

integrated in the Charlson comorbidity index. It has been reported in other studies

that there may be interactions between age and some comorbidities such as

diabetes28. Also, the Charlson comorbidity index offers the advantage of integrating

several comorbidities into one score that has been previously associated with

survival 18. Among clinical symptoms, dyspnea or shortness of breath is the most



https://doi.org/10.1101/2020.05.12.20099416


13

consistently reported symptom associated with COVID-19 severity and mortality

6,9,12,29 and was independently associated with ICU admission in this study. Obesity

is a long-recognized factor for severe pulmonary infections 30 and has been

consistently associated with adverse outcomes in COVID-19 31. Low systolic

arterial pressure and respiratory rate at triage evaluation complete the clinical

ABC-GOALS. Among the three derived scores in this study, the clinical ABC-

GOALS has an inferior performance and remains to be externally validated, as

some reported models based exclusively in demographic (but not physical

examination) information have been shown to decrease their performance after

external validation12.

The scores integrating clinical with laboratory (ABC-GOALScl) and imaging (ABC-

GOALSclx) findings seem to perform better for prediction of admission to an ICU.

Both models include values of glucose, lactate dehydrogenase and oxygenation at

admission that have been previously reported to be associated with prognosis in

COVID-19 9,11,32,33. Lactate dehydrogenase has been previously integrated into a

predictive model (see below). In contrast to other laboratory tests that may

increase during the progression of the disease, such as D-dimer and cardiac

troponin levels 34,35, LDH has proved to be the most robust laboratory predictor for

admission to ICU when evaluated at first-contact. Glucose and oxygenation were

also included in these models. Oxygenation, reflected by the S/F ratio, probably

reflects the disease extension along with the imaging findings. It is worth noting

that the model that included imaging findings (ABC-GOALSclx) only mildly improved

the model performance, therefore, it may not be necessary to perform imaging in a



https://doi.org/10.1101/2020.05.12.20099416


14

patient who is planned to be referred based on the clinical plus laboratory model

(ABC-GOALScl).

Recently, a scoring model to predict COVID-19 progression risk (the CALL score)

was derived from a Chinese population 24. This score includes the presence of any

comorbidity, age, lymphocyte count, and LDH levels. The score had a lower

performance in our cohort than in the original Chinese cohort. Most patients in our

study presented with lymphopenia below the set cut-off for the CALL score,

therefore reducing the predictive value of this parameter. Besides, we found

several other independent predictors may that increase the predictive yield for ICU

admission.

We showed that repurposing of other scores employed in pneumonia may not be

perform adequately to predict the need for admission to ICU in COVID-19

pneumonia. For example, the MuLBSTA score 22 has been validated for mortality

prediction in viral pneumonia, but may have a lower performance in COVID-19

pneumonia. This score points the number of affected lobes by viral pneumonia,

however, all patients in our study showed multi-lobar lung infiltrates reducing the

performance of this parameter. Other variables included in this score, such as

bacterial coinfection, may not perform well when evaluated at admission, as

bacterial coinfection more frequently occurs later in the disease evolution.

Finally, although the ABC-GOALS score seems to have a good performance for

mortality prediction, this data should be taken with caution as one-fourth of patients

admitted to an ICU were still on follow-up by the end of this study. Therefore, the



https://doi.org/10.1101/2020.05.12.20099416


15

prognostic performance of this score for mortality prediction should be further

evaluated in other studies.

The study has some strengths. First, all data were collected prospectively with very

few missing data for all collected variables. Second, there was an appropriate

number of patients in the group that required admission to an ICU which allowed

the study of multiple predictors. Third, the study is reported based on TRIPOD

guidelines 36.

There are limitations of this study. The study involves only derivation of the score

and therefore, the score still needs further validation in our population as well as

external validation in other populations. However, the actual need for a tool to

predict ICU admission and to timely refer patients compelled us to start using this

tool locally and to validate it over the progress of the pandemic. Another limitation

is that medical practice and admission criteria to ICU may vary between institutions

and countries, especially in stressed health systems. This score was derived from

a population living in Mexico City at >7300 feet over the sea level. The mean

partial pressure of oxygen at this altitude has been estimated at around 66mmHg

(estimated normal baseline P/F ratio ≈ 314) 37,38. We therefore used the S/F ratio to

account for respiratory compensation and the right-shift of the hemoglobin

dissociation curve that takes place at higher altitudes (higher p50 values in Mexico

City). Then, local validation is needed for this score. Finally, the thresholds set to

define the risk groups should be adapted to local needs.

In summary, the ABC-GOALS score represents a tool to evaluate patients with

COVID-19 at admission to the ED, designed to timely predict their risk of admission



https://doi.org/10.1101/2020.05.12.20099416


16

to an ICU. This score may help early referral and planning of attention during the

COVID-19 pandemic.

FUNDING

None.

CONFLICT OF INTEREST.

All authors declare there is no conflict of interest with the content of this

manuscript.

ACKNOWLEDGEMENTS.

The authors want to acknowledge all the health care team at Instituto Nacional de

Ciencias Médicas y Nutrición Salvador Zubirán at Mexico City for their

extraordinary work during this pandemic.

BIBLIOGRAPHY

1. Emanuel EJ, Persad G, Upshur R, et al. Fair Allocation of Scarce Medical

Resources in the Time of Covid-19. N Engl J Med 2020; doi:

10.1056/NEJMsb2005114



https://doi.org/10.1101/2020.05.12.20099416


17

2. World Health Organization. Naming the coronavirus disease (COVID-19)

and the virus that causes it. Available from:

www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-

guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-

it. Accessed 8May2020.

3. Wu Z, McGoogan JM. Characteristics of and Important Lessons From the

Coronavirus Disease 2019 (COVID-19) Outbreak in China. JAMA 2020;323:1239

4. Goyal P, Choi JJ, Pinheiro LC, et al. Clinical Characteristics of Covid-19 in

New York City. N Engl J Med 2020; doi:10.1056/NEJMc2010419

5. Richardson S, Hirsch JS, Narasimhan M, et al. Presenting Characteristics,

Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19

in the New York City Area. JAMA 2020; doi:10.1001/jama.2020.6775

6. Wu C, Chen X, Cai Y, et al. Risk Factors Associated With Acute Respiratory

Distress Syndrome and Death in Patients With Coronavirus Disease 2019

Pneumonia in Wuhan, China. JAMA Intern Med 2020; doi:

10.1001/jamainternmed.2020.0994

7. Hu L, Chen S, Fu Y, et al. Risk Factors Associated with Clinical Outcomes in

323 COVID-19 Hospitalized Patients in Wuhan, China. Clin Infect Dis 2020; doi:

10.1093/cid/ciaa539

8. Guan W, Ni Z, Hu Y, et al. Clinical Characteristics of Coronavirus Disease

2019 in China. N Engl J Med 2020; 382: 1708–1720



http://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it



https://doi.org/10.1101/2020.05.12.20099416


18

9. Chen R, Liang W, Jiang M, et al. Risk Factors of Fatal Outcome in

Hospitalized Subjects With Coronavirus Disease 2019 From a Nationwide Analysis

in China. Chest 2020; doi: 10.1016/j.chest.2020.04.010

10. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics

of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive

study. Lancet 2020; 395: 507–513.

11. Deng G, Yin M, Chen X, Zeng F. Clinical determinants for fatality of 44,672

patients with COVID-19. Crit Care 2020; 24: 179

12. Zhang H, Shi T, Wu X, et al. Risk prediction for poor outcome and death in

hospital in patients with COVID-19: derivation in Wuhan, China and external

validation in London, UK. medRxiv [preprint]. Available from:

https://doi.org/10.1101/2020.04.28.20082222

13. Wang K, Zuo P, Liu Y, et al. Clinical and Laboratory Predictors of In-Hospital

Mortality in Patients with COVID-19: A Cohort Study in Wuhan, China. Clin Infect

Dis 2020; doi:10.1093/cid/ciaa538

14. Yang R, Gui X, Zhang Y, Xiong Y. The role of essential organ-based

comorbidities in the prognosis of COVID-19 infection patients. Expert Rev Respir

Med 2020;1–4

15. Zhang J, Yu M, Tong S, Liu LY, Tang LV. Predictive factors for disease

progression in hospitalized patients with coronavirus disease 2019 in Wuhan,

China. J Clin Virol 2020; 127:104392



https://doi.org/10.1101/2020.05.12.20099416


19

16. Chu Y, Li T, Fang Q, Wang X. Clinical characteristics and imaging

manifestations of the 2019 novel coronavirus disease (COVID-19): A multi-center

study in Wenzhou city, Zhejiang, China. J Infect 2020; doi:

10.1016/j.jinf.2020.03.023

17. Grasselli G, Pesenti A, Cecconi M. Critical Care Utilization for the COVID-19

Outbreak in Lombardy, Italy. JAMA 2020; 323: 1545

18. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of

classifying prognostic comorbidity in longitudinal studies: Development and

validation. J Chronic Dis 1987; 40: 373–383

19. Royal College of Physicians. National Early Warning Score (NEWS) 2:

Standardising the Assessment of Acute-Illness Severity in the NHS; RCP Press:

London, UK, 2017.

20. Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ

Failure Assessment) score to describe organ dysfunction/failure. Intensive Care

Med 1996; 22: 707–710

21. Lim WS. Defining community acquired pneumonia severity on presentation

to hospital: an international derivation and validation study. Thorax 2003; 58: 377–

382

22. Guo L, Wei D, Zhang X, et al. Clinical Features Predicting Mortality Risk in

Patients With Viral Pneumonia: The MuLBSTA Score. Front Microbiol 2019;

10:2752



https://doi.org/10.1101/2020.05.12.20099416


20

23. Roca O, Messika J, Caralt B, et al. Predicting success of high-flow nasal

cannula in pneumonia patients with hypoxemic respiratory failure: The utility of the

ROX index. J Crit Care 2016; 35: 200–205

24. Ji D, Zhang D, Xu J, et al. Prediction for Progression Risk in Patients with

COVID-19 Pneumonia: the CALL Score. Clin Infect Dis 2020; doi:

10.1093/cid/ciaa414

25. Sterne JAC, White IR, Carlin JB, et al. Multiple imputation for missing data in

epidemiological and clinical research: potential and pitfalls. BMJ 2009; 338:

b2393–b2393. 26. Johns Hopkins University coronavirus resource center.

Available from: https://coronavirus.jhu.edu/map.html. Accessed 8May2020

27. Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China,

of Novel Coronavirus–Infected Pneumonia. N Engl J Med 2020; 382: 1199–1207

28. Bello-Chavolla OY, Bahena-López JP, Antonio-Villa NE, et al. Predicting

mortality due to SARS-CoV-2: a mechanistic score relating obesity and diabetes to

COVID-19 outcomes in Mexico. medRxiv [preprint]. Available from:

https://doi.org/10.1101/2020.04.20.20072223

29. Bhatraju PK, Ghassemieh BJ, Nichols M, et al. Covid-19 in Critically Ill

Patients in the Seattle Region - Case Series. N Engl J Med 2020; doi:

10.1056/NEJMoa2004500

30. Luzi L, Radaelli MG. Influenza and obesity: its odd relationship and the

lessons for COVID-19 pandemic. Acta Diabetol 2020; doi: 10.1007/s00592-020-

01522-8



https://coronavirus.jhu.edu/map.html

https://doi.org/10.1101/2020.05.12.20099416


21

31. Dietz W, Santos‐Burgoa C. Obesity and its Implications for COVID‐19

Mortality. Obesity 2020; doi: 10.1002/oby.22818

32. Levy TJ, Richardson S, Coppa K, et al. Estimating survival of hospitalized

COVID-19 patients from admission information. medRxiv [preprint]. Available from:

http://medrxiv.org/content/10.1101/2020.04.22.20075416v2.

33. Mo P, Xing Y, Xiao Y, et al. Clinical characteristics of refractory COVID-19

pneumonia in Wuhan, China. Clin Infect Dis 2020; doi: 10.1093/cid/ciaa270

34. Guo T, Fan Y, Chen M, et al. Cardiovascular Implications of Fatal Outcomes

of Patients With Coronavirus Disease 2019 (COVID-19). JAMA Cardiol 2020; doi:

10.1001/jamacardio.2020.1017

35. Wang D, Yin Y, Hu C, et al. Clinical course and outcome of 107 patients

infected with the novel coronavirus, SARS-CoV-2, discharged from two hospitals in

Wuhan, China. Crit. Care 2020; 24: 188

36. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of

a multivariable prediction model for individual prognosis or diagnosis (TRIPOD):

the TRIPOD statement. BMJ 2015; 350: g7594–g7594

37. Pérez-Padilla JR, Vázquez-García JC. Estimation of gasometric values at

different altitudes above sea level in Mexico. Rev Invest Clin 2000; 52: 148–155

38. Ramirez-Sandoval JC, Castilla-Peón MF, Gotés-Palazuelos J, et al.

Bicarbonate Values for Healthy Residents Living in Cities Above 1500 Meters of

Altitude: A Theoretical Model and Systematic Review. High Alt Med Biol 2016; 17:

85–92



https://doi.org/10.1101/2020.05.12.20099416


22

TABLES

Table 1. Subject characteristics on first evaluation at the emergency department.

All patients n=329

Critical Care n=115

General wards n=214

p-value

Age, years 49 (41-60) 53 (43-62) 49 (39-59) 0.009

Male gender, n (%) 211 (64) 88 (77) 123 (58) <0.001

Current/past smoker, n (%) 23 (7) 10 (9) 13 (6) 0.497

Comorbidities

Diabetes mellitus, n (%) 80 (24) 37 (32) 43 (20) 0.016

Hypertension, n (%) 88 (27) 33 (29) 55 (26) 0.602

Obesity, n (%) 132 (40) 59 (51) 73 (34) 0.003

CKD, n (%) 19 (6) 7 (6) 12 (6) 0.807

None, n (%) 100 (30) 18 (16) 82 (38) <0.001

Charlson index, points 1 (0-2) 1 (0-3) 1 (0-2) 0.001

0 points 141 (43) 36 (31) 105 (49) 0.002

1-2 points 125 (38) 47 (41) 78 (36)

≥3 points 63 (19) 32 (28) 31 (15)

Clinical Presentation

Fever, n (%) 302 (92) 104 (90) 198 (93) 0.532

Cough, n (%) 285 (87) 103 (90) 182 (85) 0.309

Myalgia / arthralgia, n (%) 148 (45) 40 (35) 108 (51) 0.007

Sore throat, n (%) 100 (30) 36 (31) 64 (30) 0.803

Rhinorrhea, n (%) 51 (16) 19 (17) 32 (15) 0.750

Headache, n (%) 162 (49) 56 (49) 106 (50) 0.908

Dyspnea, n (%) 229 (70) 98 (85) 131 (61) <0.001

Diarrhea, n (%) 56 (17) 21 (18) 35 (16) 0.759

Anosmia, n (%) 17 (5) 4 (4) 13 (6) 0.435

Days with symptoms 7 (5-10) 7 (5-9) 8 (5-10) 0.595

Physical examination

Body mass index, kg/m2 29.0 (26.5-31.2) 29.6 (26.9-33.4) 28.9 (26.3-30.6) 0.006

Systolic AP<100 mmHg 16 (5) 11 (10) 5 (2) 0.001

Respiratory rate, per min 24 (21-30) 28 (24-36) 24 (20-28) <0.001

Laboratory evaluation

Total leukocytes, x103/L 7.1 (5.3-9.5) 9.0 (5.8-12.1) 6.7 (5.1-8.7) <0.001

Neutrophils, x103/L 5.9 (2.8-8.2) 7.7 (4.8-10.7) 5.2 (3.6-7.1) <0.001

Lymphocytes, x103/L 0.8 (0.6-1.1) 0.7 (0.5-1.0) 0.9 (0.7-1.1) <0.001

Monocytes, x103/L 0.4 (0.3-0.6) 0.4 (0.3-0.5) 0.4 (0.3-0.6) 0.526

Hemoglobin, g/dL 15.5 (14.4-16.6) 15.5 (14.4-16.5) 15.6 (14.2-16.6) 0.843

Platelets, x103 208 (165-257) 215 (166-266) 206 (165-256) 0.515

Total bilirubin, mg/dL 0.55 (0.43-0.75) 0.62 (0.46-0.84) 0.53 (0.43-0.69) 0.005

Direct bilirubin, mg/dL 0.16 (0.11-0.22) 0.18 (0.14-0.27) 0.14 (0.11-0.21) <0.001

ALT, U/L 34.3 (23.3-52.7) 37.3 (23.9-52.7) 33.7 (22.3-52.6) 0.574

AST, U/L 39.3 (27.9-57.6) 46.6 (33.3-67.8) 36.5 (26.5-51.2) <0.001

Alkaline phosphatase, mg/dL 83 (68-105) 84 (70-107) 83 (67-103) 0.608

Serum albumin, g/dL 3.8 (3.4-4.1) 3.5 (3.2-3.9) 3.9 (2.6-4.1) <0.001

Serum globulins, g/dL 3.2 (3.0-3.5) 3.3 (3.0-3.6) 3.2 (2.9-3.5) 0.608

Glucose, mg/dL 115 (100-147) 138 (111-213) 107 (97-128) <0.001

BUN, mg/dL 14.0 (10.5-19.8) 16.6 (12.8-24.0) 12.7 (9.7-17.9) <0.001

Creatinine, mg/dL 0.93 (0.75-1.15) 1.00 (0.85-1.24) 0.89 (0.72-1.10) <0.001

Sodium, mEq/L 135 (132-137) 134 (132-138) 135 (133-137) 0.200



https://doi.org/10.1101/2020.05.12.20099416


23

Potassium, mEq/L 4.0 (3.7-4.3) 4.0 (3.6-4.3) 4.0 (3.7-4.3) 0.823

Chloride, mEq/L 101 (98-104) 100 (97-104) 102 (99-104) 0.042

Total serum CO2, mmol/L 23.2 (21.5-25.0) 22.6 (20.8-24.4) 23.4 (21.9-25.2) 0.001

Creatine kinase, U/L 103 (61-222) 142 (72-276) 88 (57-166) 0.003

Lactate dehydrogenase, U/L 336 (259-456) 474 (344-578) 288 (230-380) <0.001

C reactive protein, mg/dL 12.6 (5.4-19.3) 18.3 (12.6-25.7) 7.8 (3.4-15.6) <0.001

Ferritin, ng/mL 506 (254-939) 740 (423-1092) 375 (207-784) <0.001

Troponin I >ULN (20pg/mL) n(%) 35 (10.6) 28 (24.3) 7 (3.3) <0.001

Fibrinogen, mg/dL 604 (457-723) 688 (542-807) 499 (434-678) <0.001

D-dimer, ng/mL 628 (398-1072) 761 (495-1323) 523 (358-958) <0.001

Arterial blood gas analysis

pH 7.44 (7.42-7.47) 7.44 (7.39-7.46) 7.45 (7.42-7.47) <0.001

Bicarbonate, mmol/L 21.5 (19.9-22.9) 21.0 (18.6-23.5) 21.6 (20.5-22.8) 0.122

Lactate, mmol/L 1.2 (0.9-1.6) 1.5 (1.0-2.1) 1.1 (0.9-1.4) <0.001

PaO2/FiO2 ratio 247 (175-291) 177 (107-247) 269 (229-305) <0.001

SaO2/FiO2 ratio 330 (211-426) 193 (149-325) 410 (315-433) <0.001

Chest CT scan

Multi-lobar 329 (100) 115 (100) 214 (100) 1.000

Extension

Mild (≤20%) 57 (17) 3 (3) 54 (25) <0.001

Moderate (21-50%) 141 (43) 26 (22) 115 (54)

Severe (>50%) 131 (40) 86 (75) 45 (21)

* Continuous variables are expressed as median (interquartile range).

Abbreviations. CKD, chronic kidney disease; AP, arterial pressure; ALT, alanine transaminase; AST,

aspartate transaminase; BUN, blood urea nitrogen; CO2, total diluted carbon dioxide; PaO2/FiO2 ratio,

ratio of arterial pressure of oxygen to fraction of inspired oxygen; SaFiO2/FiO2, ratio of the percentage

of hemoglobin saturation by oxygen to fraction of inspired oxygen; NEWS-2, National Early Warning

Score 2; SOFA – PaO2/FiO2, Sequential Organ Failure Assessment score with respiratory points

obtained by PaO2/FiO2 ratio; SOFA-SaO2/FiO2, Sequential Organ Failure Assessment score with

respiratory points obtained by SaO2/FiO2.



https://doi.org/10.1101/2020.05.12.20099416


24

Table 2. Models to predict the risk of admission to an intensive care unit

Bivariate Multivariate OR 95%CI p-value OR 95%CI p-value

Model 1: clinical Male gender 2.41 1.45-4.01 0.001 1.82 1.01-3.30 0.049 Obesity, BMI>30kg/m2 2.04 1.28-3.23 0.003 2.56 1.56-4.55 0.001 Dyspnea 3.65 2.04-6.55 <0.001 2.39 1.22-4.67 0.022 SBP<100mmHg 4.42 1.50-13.06 0.007 5.10 1.37-18.9 0.015 Respiratory rate <24 per min 1.00 reference reference 1.00 reference reference 24-28 per min 2.253 1.223-4.150 0.009 2.12 1.08-4.17 0.029 >28 per min 8.063 4.279-15.19 <0.001 5.14 2.50-10.7 <0.001 Charlson index 0 points 1.00 reference reference 1.00 reference reference 1-2 points 1.76 1.04-2.97 0.035 2.33 1.28-4.27 0.006 ≥3 points 3.01 1.62-5.61 0.001 4.96 2.32-10.6 <0.001 Model 2: clinical + laboratory Male gender 2.41 1.45-4.01 0.001 2.48 1.28-4.81 0.007 Obesity, BMI>30kg/m2 2.04 1.28-3.23 0.003 2.97 1.59-5.55 0.001 Dyspnea 3.65 2.04-6.55 <0.001 2.24 1.08-4.61 0.029 Charlson index ≥3 2.28 1.30-3.98 0.004 2.84 1.31-6.15 0.008 SBP<100mmHg 4.42 1.50-13.06 0.007 5.71 1.31-24.9 0.020 Glucose >200g/dL 3.10 1.67-5.74 <0.001 3.00 1.35-6.67 0.007 Albumin <3.5g/dL 4.15 2.52-6.83 <0.001 2.13 1.14-3.99 0.018 LDH>ULN 8.55 4.11-17.79 <0.001 3.39 1.47-7.79 0.004 S/F ratio<300 8.48 5.07-14.19 <0.001 5.41 2.93-9.97 <0.001 Model 3: clinical + laboratory + image Male gender 2.41 1.45-4.01 0.001 2.14 1.09-4.18 0.026 Obesity, BMI>30kg/m2 2.04 1.28-3.23 0.003 2.58 1.37-4.85 0.003 Charlson index ≥3 2.28 1.30-3.98 0.004 2.90 1.32-6.37 0.008 SBP<100mmHg 4.42 1.50-13.06 0.007 5.15 1.12-23.6 0.035 Glucose >200mg/dL 3.10 1.67-5.74 <0.001 2.64 1.14-6.12 0.023 LDH>ULN 8.55 4.11-17.79 <0.001 2.83 1.20-6.66 0.017 S/F ratio<300 8.48 5.07-14.19 <0.001 4.43 2.35-8.35 <0.001 CT lung involvement >50% 11.1 6.53-18.99 <0.001 4.62 2.47-8.64 <0.001

Abbreviations. OR, odds ratio; 95%CI, 95% confidence intervals; SBP, systolic blood pressure; LDH,

lactate dehydrogenase serum levels; S/F ratio, ratio of percentage of hemoglobin saturation to

fraction of inspired oxygen; CT, computed tomography.



https://doi.org/10.1101/2020.05.12.20099416


25

Table 3. ABC-GOALS scoring system

POINTS

Parameter ABC-GOALS clinic

ABC-GOALS clinic + laboratory

ABC-GOALS + imaging

Male sex 1 1 1 A rterial BP Systolic <100mmHg 4 4 4 B reathlessness 1 1 - C harlson index 1-2 points 1 - - ≥ 3 points 3 2 2 G lucose > 200 mg/dL - 2 2 O besity, BMI >30 kg/m2 2 2 2 A lbumin < 3.5 g/dL - 1 - L actate dehydrogenase >ULN - 2 2 S aO2 / FiO2 ratio < 300 - 4 3

CT lung involvement > 50% - - 4 Respiratory Rate 24-28 per minute 1 - - > 28 per minute 4 - -

TOTAL SCORE 0-15 0-19 0-20

RISK CATEGORIES Low Mean risk (95% CI)

0-2 points 8% (7%-9%)

0-3 points 4% (3%-4%)

0-4 points 6% (5%-6%)

Moderate Mean risk (95% CI)

3-7 points 32% (30%-34%)

4-9 points 30% (27%-32%)

5-9 points 32% (29%-34%)

High Mean risk (95% CI)

≥8 points 73% (70%-75%)

≥10 points 80% (78%-82%)

≥10 points 80% (78%-82%)



https://doi.org/10.1101/2020.05.12.20099416


26

Table 4. Predictive performance of all evaluated scores for admission to an ICU.

Score Area under the curve

Cut-off Sensitivity Specificity

ABC-GOALSc 0.79 (0.74-0.83) ≥5 74 68

ABC-GOALScl 0.86 (0.82-0.90) ≥6 ≥7

89 78

67 79

ABC-GOALSclx 0.88 (0.84-0.92) ≥6 ≥7

88 85

72 77

NEWS-2 0.76 (0.70-0.81) ≥7 ≥9

77 41

60 88

CALL 0.72 (0.67-0.78) ≥9 ≥10

75 67

56 67

SOFA 0.77 (0.71-0.82) ≥1 ≥2

85 64

54 80

CURB-65 0.71 (0.65-0.77) ≥1 74 63

MuLBSTA 0.63 (0.57-0.70) ≥8 ≥9 ≥12

69 64 12

54 67 94

ROX index 0.81 (0.76-0.86) ≤5 38 97

* All scores were obtained at admission to the emergency room. SOFA respiratory points were assigned based on the S/F ratio.

Abbreviations. ABC-GOALSc, score including only clinical variables; ABC-GOALScl, score including both clinical and laboratory variables; ABC-GOALSclx, score including clinical, laboratory and x-ray findings; NEWS-2, National Early Warning Score version 2; CALL, comorbidity, age, lymphocyte, LDH score for predicting progression of COVID-19; SOFA, Sequential Organ Failure Assessment score; CURB-65, confusion, urea, respiratory rate, blood pressure, age>65 years score for risk of mortality from pneumonia; MuLBSTA, Multi-lobar infiltrate, Lymphocyte count, Bacterial co-infection, Smoking history, hypertension, Age>65 years score for mortality from viral pneumonia; ROX index, S/F ratio divided by the respiratory rate to predict the risk of intubation in hypoxemic respiratory failure.



https://doi.org/10.1101/2020.05.12.20099416


27

FIGURES

Figure 1. Receiver-operating curves for the three ABC-GOALS scores.



https://doi.org/10.1101/2020.05.12.20099416


28

Figure 2. Predicted and observed percentages of patients admitted to an ICU by

each point increment in the ABC-GOALSc (A), ABC-GOALScl (B) and ABC-GOALSclx

(C) scores.



https://doi.org/10.1101/2020.05.12.20099416


29

SUPPLEMENTARY MATERIAL

Supplementary Table 1. Characteristics at admission to the emergency department of all the

included subjects.

All patients

n=329 Critical Care

n=115 General wards

n=214 p-value

Age, years 49 (41-60) 53 (43-62) 49 (39-59) 0.009

Male gender, n (%) 211 (64) 88 (77) 123 (58) <0.001

Current/past smoker, n (%) 23 (7) 10 (9) 13 (6) 0.497

Comorbidities

Diabetes mellitus, n (%) 80 (24) 37 (32) 43 (20) 0.016

Hypertension, n (%) 88 (27) 33 (29) 55 (26) 0.602

Obesity, n (%) 132 (40) 59 (51) 73 (34) 0.003

CKD, n (%) 19 (6) 7 (6) 12 (6) 0.807

None, n (%) 100 (30) 18 (16) 82 (38) <0.001

Charlson index, points 1 (0-2) 1 (0-3) 1 (0-2) 0.001

0 points 141 (43) 36 (31) 105 (49) 0.002

1-2 points 125 (38) 47 (41) 78 (36)

≥3 points 63 (19) 32 (28) 31 (15)

Clinical Presentation

Fever, n (%) 302 (92) 104 (90) 198 (93) 0.532

Cough, n (%) 285 (87) 103 (90) 182 (85) 0.309

Myalgia / arthralgia, n (%) 148 (45) 40 (35) 108 (51) 0.007

Sore throat, n (%) 100 (30) 36 (31) 64 (30) 0.803

Rhinorrhea, n (%) 51 (16) 19 (17) 32 (15) 0.750

Headache, n (%) 162 (49) 56 (49) 106 (50) 0.908

Dyspnea, n (%) 229 (70) 98 (85) 131 (61) <0.001

Diarrhea, n (%) 56 (17) 21 (18) 35 (16) 0.759

Anosmia, n (%) 17 (5) 4 (4) 13 (6) 0.435

Days with symptoms 7 (5-10) 7 (5-9) 8 (5-10) 0.595

Physical examination

Body mass index, kg/m2 29.0 (26.5-31.2) 29.6 (26.9-33.4) 28.9 (26.3-30.6) 0.006

Mean AP, mmHg 90 (83-98) 89 (82-97) 91 (84-99) 0.152

Systolic AP<100 mmHg 16 (5) 11 (10) 5 (2) 0.001

Respiratory rate, per min 24 (21-30) 28 (24-36) 24 (20-28) <0.001

<24 per minute 122 (37) 21 (18) 101 (47) <0.001

24-28 per minute 116 (35) 37 (32) 79 (37)

>28 per minute 91 (28) 57 (50) 34 (16)

Laboratory evaluation

Total leukocytes, x103/L 7.1 (5.3-9.5) 9.0 (5.8-12.1) 6.7 (5.1-8.7) <0.001

Neutrophils, x103/L 5.9 (2.8-8.2) 7.7 (4.8-10.7) 5.2 (3.6-7.1) <0.001

Lymphocytes, x103/L 0.8 (0.6-1.1) 0.7 (0.5-1.0) 0.9 (0.7-1.1) <0.001

Monocytes, x103/L 0.4 (0.3-0.6) 0.4 (0.3-0.5) 0.4 (0.3-0.6) 0.526

Hemoglobin, g/dL 15.5 (14.4-16.6) 15.5 (14.4-16.5) 15.6 (14.2-16.6) 0.843

Platelets, x103 208 (165-257) 215 (166-266) 206 (165-256) 0.515

Total bilirubin, mg/dL 0.55 (0.43-0.75) 0.62 (0.46-0.84) 0.53 (0.43-0.69) 0.005

Direct bilirubin, mg/dL 0.16 (0.11-0.22) 0.18 (0.14-0.27) 0.14 (0.11-0.21) <0.001

ALT, U/L 34.3 (23.3-52.7) 37.3 (23.9-52.7) 33.7 (22.3-52.6) 0.574

AST, U/L 39.3 (27.9-57.6) 46.6 (33.3-67.8) 36.5 (26.5-51.2) <0.001

Alkaline phosphatase, mg/dL 83 (68-105) 84 (70-107) 83 (67-103) 0.608



https://doi.org/10.1101/2020.05.12.20099416


30

Serum albumin, g/dL 3.8 (3.4-4.1) 3.5 (3.2-3.9) 3.9 (2.6-4.1) <0.001

<3.5 g/dL, n (%) 98 (30) 57 (50) 41 (19) <0.001

Serum globulins, g/dL 3.2 (3.0-3.5) 3.3 (3.0-3.6) 3.2 (2.9-3.5) 0.608

Glucose, mg/dL 115 (100-147) 138 (111-213) 107 (97-128) <0.001

>200 mg/dL, n (%) 50 (15) 29 (25) 21 (10) <0.001

BUN, mg/dL 14.0 (10.5-19.8) 16.6 (12.8-24.0) 12.7 (9.7-17.9) <0.001

Creatinine, mg/dL 0.93 (0.75-1.15) 1.00 (0.85-1.24) 0.89 (0.72-1.10) <0.001

Sodium, mEq/L 135 (132-137) 134 (132-138) 135 (133-137) 0.200

Potassium, mEq/L 4.0 (3.7-4.3) 4.0 (3.6-4.3) 4.0 (3.7-4.3) 0.823

Chloride, mEq/L 101 (98-104) 100 (97-104) 102 (99-104) 0.042

Total serum CO2, mmol/L 23.2 (21.5-25.0) 22.6 (20.8-24.4) 23.4 (21.9-25.2) 0.001

Corrected calcium, mg/dL 8.6 (8.5-9.0) 8.6 (8.4-8.9) 8.7 (8.5-9.0) <0.001

Phosphorus, mg/dL 3.2 (2.8-3.7) 3.3 (2.7-3.9) 3.2 (2.8-3.6) 0.114

Magnesium, mg/dL 2.1 (2.0-2.3) 2.2 (2.0-2.4) 2.1 (2.0-2.3) 0.076

Creatine kinase, U/L 103 (61-222) 142 (72-276) 88 (57-166) 0.003

Lactate dehydrogenase, U/L 336 (259-456) 474 (344-578) 288 (230-380) <0.001

> ULN (271 U/L), n (%) 230 (70) 106 (92) 124 (58) <0.001

C reactive protein, mg/dL 12.6 (5.4-19.3) 18.3 (12.6-25.7) 7.8 (3.4-15.6) <0.001

Ferritin, ng/mL 506 (254-939) 740 (423-1092) 375 (207-784) <0.001

Troponin I, pg/mL 4.6 (2.9-8.5) 7.8 (4.9-19.4) 3.7 (2.6-5.6) <0.001

>ULN (20pg/mL), n (%) 35 (10.6) 28 (24.3) 7 (3.3) <0.001

Fibrinogen, mg/dL 604 (457-723) 688 (542-807) 499 (434-678) <0.001

D-dimer, ng/mL 628 (398-1072) 761 (495-1323) 523 (358-958) <0.001


pH 7.44 (7.42-7.47) 7.44 (7.39-7.46) 7.45 (7.42-7.47) <0.001

Bicarbonate, mmol/L 21.5 (19.9-22.9) 21.0 (18.6-23.5) 21.6 (20.5-22.8) 0.122

Lactate, mmol/L 1.2 (0.9-1.6) 1.5 (1.0-2.1) 1.1 (0.9-1.4) <0.001

PaO2/FiO2 ratio 247 (175-291) 177 (107-247) 269 (229-305) <0.001

SaO2/FiO2 ratio 330 (211-426) 193 (149-325) 410 (315-433) <0.001

<300, n (%) 124 (38) 80 (70) 44 (21) <0.001

Scores at presentation

NEWS-2, points 7 (5-8) 8 (7-10) 6 (4-7) <0.001

SOFA - PaO2/FiO2 2 (2-3) 3 (2-4) 2 (2-3) <0.001

SOFA - SaO2/FiO2 1 (0-2) 2 (1-3) 0 (0-1) <0.001

CALL score, points 9 (7-11) 10 (8-11) 8 (7-10) <0.001

CURB-65 score, points 0 (0-1) 1 (0-2) 0 (0-1) <0.001

MuLBSTA score, points 9 (5-11) 9 (5-11) 7 (5-9) <0.001

ROX index 13.6 (7.1-18.6) 6.9 (4.5-12.9) 16.4 (12.3-20.7) <0.001

Chest CT scan

Multi-lobar, n (%) 329 (100) 115 (100) 214 (100) 1.000

Extension, n (%)

Mild (≤20%) 57 (17) 3 (3) 54 (25) <0.001

Moderate (21-50%) 141 (43) 26 (23) 115 (54)

Severe (>50%) 131 (40) 86 (75) 45 (21)

Abbreviations. CKD, chronic kidney disease; AP, arterial pressure; ALT, alanine transferase; AST, aspartate

transferase; BUN, blood urea nitrogen; CO2, total diluted carbon dioxide; PaO2/FiO2 ratio, ratio of arterial pressure of

oxygen to fraction of inspired oxygen; SaFiO2/FiO2, ratio of the percentage of hemoglobin saturation by oxygen to

fraction of inspired oxygen; NEWS-2, National Early Warning Score 2; SOFA – PaO2/FiO2, Sequential Organ Failure

Assessment score with respiratory points obtained by PaO2/FiO2 ratio; SOFA-SaO2/FiO2, Sequential Organ Failure

Assessment score with respiratory points obtained by SaO2/FiO2; MuLBSTA, Multi-lobar infiltrate, Lymphocyte count,

Bacterial co-infection, Smoking history, hypertension, Age>65 years score for mortality from viral pneumonia; ROX

index, S/F ratio divided by the respiratory rate to predict the risk of intubation in hypoxemic respiratory failure ULN,

upper limit of normal.



https://doi.org/10.1101/2020.05.12.20099416


31

Supplementary Table 2. Variables explored by bivariate logistic regression analysis.

OR 95% CI p-value

Demographics variables

Age, per year 1.021 1.004-1.038 0.015*

Age ≥50 years, vs. less 1.716 1.085-2.713 0.021*

Male gender, vs. female 2.411 1.449-4.013 0.001*

Previous medical history

Current or previous smoker, vs. no 1.473 0.625-3.471 0.376

Hypertension, vs. no 1.163 0.701-1.932 0.559

Diabetes mellitus, vs. no 1.886 1.128-3.156 0.016*

Chronic kidney disease, vs. no 1.122 0.429-2.935 0.814

Obesity (BMI>30kg/m2), vs. no 2.035 1.282-3.230 0.003*

No comorbidities, vs. yes 0.299 0.168-0.530 0.000*

Charlson index, per point 1.235 1.071-1.425 0.004*

Charlson index categories

0 points 1.000 reference reference

1-2 points 1.757 1.041-2.967 0.035

≥ 3 points 3.011 1.616-5.610 0.001

Clinical presentation variables

Days with symptoms, per day 1.010 0.956- 1.066 0.729

Three of more days with symptoms 3.859 0.857-17.38 0.079

Fever, vs. none 0.764 0.342-1.706 0.511

Cough, vs. none 1.509 0.745-3.058 0.253*

Fatigue/malaise, vs. none 0.907 0.576-1.428 0.673

Mialgia/arthralgia, vs. none 0.523 0.328-0.836 0.007*

Sore throath, vs. none 1.068 0.654-1.745 0.793

Rinorrhea, vs. none 1.126 0.606-2.091 0.708

Headache, vs. none 0.967 0.615-1.522 0.885

Dyspnea, vs. none 3.652 2.037-6.548 0.000*

Diarrhea, vs. none 1.143 0.630-2.073 0.661

Anosmia, vs. none 0.557 0.177-1.750 0.316

Physical examination variables

Mean arterial pressure, per mmHg 0.989 0.971-1.007 0.233

MAP<65mmHg, vs. higher 3.770 0.338-42.03 0.281

Systolic arterial pressure, per mmHg 0.991 0.978-1.004 0.184

SAP<100mmHg, vs. higher 4.421 1.497-13.06 0.007*

Diastolic arterial pressure, per mmHg 0.991 0.973-1.010 0.369

Respiratory rate, per 1x’ 1.137 1.094-1.183 0.000*

Respiratory rate

<24 per minute 1.000 reference reference

24-28 per minute 2.253 1.223-4.150 0.009

>28 per minute 8.063 4.279-15.19 0.000*

Body mass index, per kg/m2 1.080 1.030-1.132 0.001*

Laboratory variables

Total leukocytes, per 1x103 1.209 1.127-1.296 0.000*

Neutrophils, per 1x103 1.000 1.000-1.000 0.000*

Lymphocytes, per 1x103 0.999 0.998-1.000 0.001*

Monocytes, per 1x103 1.001 0.999-1.001 0.596

Hemoglobin, per g/dL 0.973 0.860-1.101 0.666



https://doi.org/10.1101/2020.05.12.20099416


32

Platelets, per 1x106 1.001 0.998-1.004 0.559

Total bilirubin, per mg/dL 1.753 0.999-3.076 0.050

Direct bilirubin, per mg/dL 0.963 0.806-1.151 0.681

ALT, per U/L 1.002 0.999-1.005 0.256

AST, per U/L 1.005 1.000-1.010 0.033*

AST ≥40U/L, vs. lower 2.310 1.452-3.676 0.000*

Alkaline phosphatase, per mg/dL 1.001 0.996-1.006 0.773

Serum albumin, per mg/dL 0.166 0.094-0.294 0.000*

Serum albumin <3.5mg/dL, vs. higher 4.147 2.516-6.834 0.000*

Serum globulins, per mg/dL 1.341 0.812-2.215 0.252

Glucose, per mg/dL 1.008 1.005-1.012 0.000*

Glucose > 200 mg/dL, vs. lower 3.099 1.673-5.740 0.000*

Blood urea nitrogen, per mg/dL 1.048 1.023-1.073 0.000*

Serum creatinine, per mg/dL 1.638 1.003-2.676 0.048*

Sodium, per mEq/L 0.967 0.914-1.024 0.253

Potasium, per mEq/L 1.124 0.853-1.481 0.405

Chloride, per mEq/L 0.945 0.894-0.997 0.040*

Total CO2, per mmol/L 0.873 0.803-0.948 0.001*

Corrected calcium, per mg/dL 1.008 0.851-1.194 0.927

Phosphorus, per mg/dL 1.452 1.091-1.931 0.011*

Magnesium per mg/dL 1.821 0.875-3.793 0.109

Creatine kinase, per U/L 1.001 1.000-1.002 0.067

Lactate dehydrogenase, per U/L 1.006 1.004-1.07 0.000*

≥ upper limit of normal, vs. lower 8.548 4.109-17.79 0.000*

C-reactive protein, per mg/dL 1.114 1.082-1.148 0.000*

≥5mg/dL, vs. lower 29.252 7.026-121.8 0.000*

≥10mg/dL, vs. lower 7.942 4.438-14.215 0.000*

Ferritin, per ng/mL 1.001 1.000-1.001 0.000*

≥400ng/mL, vs. lower 3.579 2.154-5.947 0.000*

Troponin I, per pg/mL 1.006 1.000-1.012 0.047*

≥upper limit of normal, vs. lower 9.471 3.987-22.501 0.000*

Fibrinogen, per mg/dL 1.004 1.002-1.005 0.000*

≥500ng/mL, vs. lower 3.249 1.963-5.376 0.000*

D dimer, per ng/mL 1.000 1.000-1.000 0.036*

≥500ng/mL, vs. lower 2.620 1.589-4.318 0.000*


pH, per unit 0.000 0.000-0.003 0.000*

Bicarbonate, per mmol/L 0.948 0.880-1.021 0.948

Lactate, per mmol/L 2.352 1.620-3.415 0.000*

P/F ratio, per unit 0.989 0.985-0.992 0.000*

S/F ratio, per unit 0.990 0.988-0.992 0.000*

S/F ratio<300, vs. higher 8.831 5.264-14.815 0.000*

Scores

NEWS-2, per point 1.604 1.409-1.826 0.000*

SOFA – PaO2/FiO2, per point 1.911 1.551-2.355 0.000*

SOFA – SaO2/FiO2, per point 2.090 1.711-2.551 0.000*

CALL score, per point 1.483 1.311-1.677 0.000*

CURB-65, per point 2.409 1.831-3.170 0.000*

ROX index, per point 0.822 0.785-0.862 0.000*

MuLBSTA score, per point 1.200 1.099-1.310 0.000*

Chest CT scan



https://doi.org/10.1101/2020.05.12.20099416


33

Pneumonia extension

Mild-moderate (<50%) 1.000 reference Reference

Severe (>50%) 11.137 6.529-18.99 <0.001

Abbreviations. BMI, body mass index; ALT, alanine aminotransferase; AST, aspartate aminotransferase; P/F

ratio, ratio of arterial pressure of oxygen to fraction of inspired oxygen; S/F ratio, ratio of oxygen hemoglobin

saturation to fraction of inspired oxygen; NEWS-2, National Early Warning Score 2; SOFA – PaO2/FiO2,

Sequential Organ Failure Assessment score with respiratory points obtained by PaO2/FiO2 ratio; SOFA-

SaO2/FiO2, Sequential Organ Failure Assessment score with respiratory points obtained by SaO2/FiO2,

MuLBSTA, Multi-lobar infiltrate, Lymphocyte count, Bacterial co-infection, Smoking history, hypertension,

Age>65 years score for mortality from viral pneumonia; ROX index, S/F ratio divided by the respiratory rate to

predict the risk of intubation in hypoxemic respiratory failure.



https://doi.org/10.1101/2020.05.12.20099416


Documents

DERIVATION OF A SCORE TO PREDICT ADMISSION TO … · 5/12/2020 · 1 DERIVATION OF A SCORE TO PREDICT ADMISSION TO INTENSIVE CARE UNIT IN PATIENTS WITH COVID-19: THE ABC-GOALS SCORE